Event-driven Data Mining Techniques for Automotive Fault Diagnosis

Abstract

The increasing sophistication of electronics in vehicular systems is providing the necessary information to perform data-driven diagnostics. Specifically, the advances in automobiles enable periodic acquisition of data from telematics services and the associated dealer diagnostic data from vehicles; this requires a data-driven framework that can detect component degradations and isolate the root causes of failures. The event-driven data consists of diagnostic trouble codes (DTCs) and the concomitant parameter identifiers (PIDs) collected from various sensors, customer complaints (CCs), and labor codes (LCs) associated with the repair. In this paper, we discuss a systematic data-driven diagnostic framework featuring data pre-processing, data visualization, clustering, classification, and fusion techniques and apply it to field failure datasets. The results demonstrated that the support vector machine (SVM) classifier with DTCs and customer complaints as features provides the best accuracy (74.3%) compared to any other classifier and that a tree- structured classifier with SVM as the base classifier at each node achieves approximately 75.2% diagnostic accuracy.